Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models

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Abstract

Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research.
Original languageEnglish
Article number611
Pages (from-to)1-19
Number of pages19
JournalBiosensors
Volume14
Issue number12
Early online date13 Dec 2024
DOIs
Publication statusPublished (in print/issue) - 31 Dec 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Data Access Statement

Data underlying this research is owned and managed by Ulster University, UK, in accordance with institutional data management policies ensuring ethical, legal,
and professional compliance for access.

Keywords

  • thyroid-stimulating hormone (TSH)
  • convolutional neural network (CNN)
  • lateral flow assay (LFA)
  • point-of-care (POC)
  • region of interest (ROI)
  • gray-level co-occurrence matrix (GLCM)
  • texture analysis
  • Convolutional Neural Network (Cnn)
  • Neural Networks, Computer
  • Texture analysis
  • Lateral Flow Assay (Lfa)
  • Artificial Intelligence
  • Humans
  • Point-of-care (Poc)
  • Gray-level Co-occurrence Matrix (Glcm)
  • Thyroid-stimulating Hormone (Tsh)
  • Thyroid Gland
  • Algorithms
  • Point-of-Care Systems
  • Region Of Interest (Roi)
  • Biosensing Techniques
  • Thyrotropin
  • Thyrotropin/analysis

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